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Although every individual has a unique biology, most medicine still relies on the one-size-fits-all approach, which often fails in the treatment of heterogeneous diseases like cancer. An emerging approach to disease treatment is precision medicine, in which a specific treatment is tailored for individual patients using their biological information, including their genome, phenome, and proteome. Two clinical actions are important for implementing precision medicine in cancer therapies: choosing the correct drugs via patient stratification and choosing a suitable drug dosage and duration via drug response monitoring.

After selecting the potential drug candidate, it is crucial to monitor tumor response to drug therapy because cancer is a dynamic disease that can develop drug resistance. Although non-invasive tumor imaging techniques such as magnetic resonance imaging, computed tomography, and positron emission tomography can assess physical size and metabolic activity of tumors, these techniques have poor time resolution and cannot capture the dynamic changes of bio-molecules implicated with drug resistance. Thus, to effectively monitor drug response, supplemental diagnostic or prognostic markers must be routinely measured from patient biopsies. Unfortunately, routine monitoring of multiple biomarkers from patient biopsies is impractical, as conventional analytical assays require large sample amounts (up to 100-1,000 mg of tissue or 10 mL of blood).

In response to this challenge, this thesis describes the development of various microfluidic technologies that can perform multiplexed measurements (up to 20-plex) using minute amounts of sample (10,000-100,000 cells or 30µL of blood) in a miniaturized analytical platform (maximum 75 × 26 × 1 mm footprint). We applied these technologies for drug screening and drug response monitoring in glioblastoma multiforme, a highly lethal brain tumor, assaying two different types of patient biopsies: cancer cells and blood.

First, we developed an integrated microfluidics-chip/beta particle imaging system that can screen for effective therapies using small amounts of patient-derived cell lines. Since glioblastoma cells have abnormally high glycolytic activity, this was used as a read-out for drug response. Single cells were isolated in micro-traps, and their glycolytic activity was quantitated using a radioactive probe. This platform can assess potential drug targets directly from patient biopsies without administering drugs to the patient.

Second, we developed an in vitro diagnostic test that can monitor tumor drug resistance by measuring up to 14 proteins in finger-prick volumes of blood. This test relies on microfluidics and microarray patterning of antibodies to carry out multiplexed sandwich-type immunofluorescence assays. Using this technology and conventional tumor imaging techniques, we linked proteomic signatures to tumor growth, establishing diagnostic and prognostic models in two clinical treatment cases of bevacizumab and buparlisib. Moreover, we adopted the multiplexed proteomic measurement platform to rapidly screen out small peptide binding agents that target an oncogenic protein in glioblastoma.

The microfluidic tools developed here are sample-efficient and highly informative, and we propose that these techniques could enable routine evaluation of drug response in a precision medicine workflow.

Pertenece a



Kim, Jungwoo - 

Id.: 70082643

Versión: 1.0

Estado: Final

Tipo:  application/pdf - 

Tipo de recurso: Thesis  -  NonPeerReviewed  - 

Tipo de Interactividad: Expositivo

Nivel de Interactividad: muy bajo

Audiencia: Estudiante  -  Profesor  -  Autor  - 

Estructura: Atomic

Coste: no

Copyright: sí

Formatos:  application/pdf - 

Requerimientos técnicos:  Browser: Any - 

Relación: [References] http://resolver.caltech.edu/CaltechTHESIS:09042017-140704721
[References] https://thesis.library.caltech.edu/10411/

Fecha de contribución: 09-may-2018


* Kim, Jungwoo (2018) Microfluidic Analysis in Patient Biopsies: toward Precision Medicine for Glioblastoma Multiforme. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/Z9639MX2. http://resolver.caltech.edu/CaltechTHESIS:09042017-140704721

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